Personalizing Matrix Assisted Autologous Chondrocyte Implantation

描述（由申请人提供）：自体软骨细胞植入（ACI）治疗膝关节软骨损伤的成功率很低，仅限于年轻、健康和活跃的患者。随着被称为矩阵辅助ACI（MACI）的第二代ACI的出现，出现了新的机会。我们假设，如果矩阵的设计是患者特异性的（即，具体到细胞的组织合成能力），不仅可以改善ACI的长期有效性，而且可以将其适应症扩展到更广泛的患者群体，而无论年龄或健康状况如何。因此，本研究项目的总体目标是个性化MACI。我们个性化MACI的创新方法结合了以下两个高度相互关联的主题：（a）一类新的高度可调水凝胶，具有时空降解控制（以实现患者匹配的组织合成能力），高模量能力（以恢复功能）和基质保留能力（以最大限度地减少组织损失）。(b)引入基于完善的理论框架的通用计算工具，该工具将分析与患者特定细胞的响应相关的数据，并基于此信息预测相应的水凝胶结构和降解，从而使组织能够在动态负载环境（例如膝关节）中生长和持续的机械完整性。为了实现我们的总体研究目标，具体目标如下。我们的目标是确定模型常数，使个性化的水凝胶的设计，首先在机械负载的情况下（目标1），然后在机械负载的存在下（目标2）。我们将通过一个综合的实验和模拟活动结合使用一个自我学习算法来实现这一点。这将导致数据驱动的预测计算模型的构建。一旦开发出来，我们将使用大型动物模型测试数学模型在个性化MACI中的预测能力，特别是治疗猪膝关节中的软骨病变（目标3）。完成时 在这个为期五年的研究项目中，我们希望开发出一种预测性的计算工具，并建立一种新颖的、高度可调的水凝胶平台，用于个性化MACI。计算预测工具的通用性使其能够在未来的研究中广泛应用于其他支架和细胞，包括骨关节炎软骨细胞和干细胞。

DESCRIPTION (provided by applicant): Success of Autologous Chondrocyte Implantation (ACI) for treating damaged cartilage in the knee has been marginal and limited to young, healthy, and active patients. With the advent of second generation ACI referred to as Matrix-Assisted ACI (MACI), a new opportunity arises. We hypothesize that if the design of the matrix is patient-specific (i.e., specific to the tissue synthesis capabilities of the cell), it will be pssible to not only improve the effectiveness of ACI long-term, but expand its indication to a wider patient population regardless of age or health. Thus, the overarching goal of this research project is to personalize MACI. Our innovative approach to personalizing MACI combines the following two highly interconnected themes: (a) A new class of highly tunable hydrogels with spatiotemporal control over degradation (to enable patient-matched tissue synthesis capabilities), high moduli capabilities (to restore function), and matrix-retention capabilities (t minimize tissue loss). (b) The introduction of a universal computational tool based on a well-established theoretical framework, which will analyze data related to the response of a patient-specific cell and, based on this information, predict the corresponding hydrogel structure and degradation that enables tissue growth and sustained mechanical integrity in a dynamic loading environment (such as that in the knee). To accomplish our overall research goals, the specific aims are as follows. We aim to determine model constants that enable the design of personalized hydrogels, first in the absence of mechanical loading (Aim 1) then in the presence of mechanical loading (Aim 2). We will accomplish this through an integrated experimental and simulation campaign combined with the use of a self-learning algorithm. This will lead to the construction of the data- driven predictive computational model. Once developed, we will test the predictive capability of the mathematical model in personalized MACI using a large animal model, specifically to treat a chondral lesion in the knee of a swine (Aim 3). At the completion of this five year research project, we expect to have developed a predictive computational tool and established a novel and highly tunable hydrogel platform for personalizing MACI. The universal nature of the computational predictive tool enables it to be broadly applied in future research to other scaffolds and cells, including osteoarthritic chondrocytes and stem cells.